1.4 Billion Text Credentials Analysis (NLP)

Using deep learning and NLP to analyze a large corpus of clear text passwords.

Objectives:

• Train a generative model.
• Understand how people change their passwords over time: hello123 -> h@llo123 -> h@llo!23.

Disclaimer: for research purposes only.

In the press

• 1.4 Billion Clear Text Credentials Discovered in a Single Database
• Collection of 1.4 Billion Plain-Text Leaked Passwords Found Circulating Online
• Archive of 1.4 BEEELLION credentials in clear text found in dark web archive
• Forbes

Get the data

• Download any Torrent client.
• Here is a magnet link you can find on Reddit:
  • magnet:?xt=urn:btih:7ffbcd8cee06aba2ce6561688cf68ce2addca0a3&dn=BreachCompilation&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Ftracker.leechers-paradise.org%3A6969&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fglotorrents.pw%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337

Deep Learning

• Stay tuned!

Map the password list for each email

Generate the JSON files containing emails <-> list of passwords. Output folder is ~/BreachCompilationAnalysis.

python3 read.py --breach_compilation_folder ~/BreachCompilation

• Make sure you have enough free memory (8GB should be enough).
• It took 1h30m to run on a Intel(R) Core(TM) i7-6900K CPU @ 3.20GHz (on a single thread).
• Uncompressed output is 13G.

Output is of the form:

> less ReducePasswordsOnSimilarEmailsCallback-z-b.json # emails starting with zb.
{
    "zb-email1@yahoo.com": [
        "pass1",
        "pass2"
    ],
    "zb-email2@yahoo.com": [
        "pass1",
        "pass2",
        "pass3"
    ],
    [...]
}